Functional magnetic resonance imaging (fMRI) is increasingly used for pre-surgical planning, behavioral assessment, and research into neurological and psychiatric illnesses and brain function. However, subject motion and observation noise remain significant impediments to obtaining high-quality fMRI time series data. The proposed research focuses on developing techniques for adjusting the orientation and other parameters of the fMRI acquisition and reconstruction to the noise level of the data and the detected patient motion in real- time. Our first aim is twofold: (1) to modify fMRI pulse sequences to automatically reorient the acquired volume to compensate for pre-measured motion and (2) to denoise the acquired data using sparsity-based reconstruction guided by the measured noise level. Initially, we use motion-tracking devices and noise-only pre-scans to measure motion and estimate the noise variance; our second aim is to predict motion and estimate the noise level from previous frames in the time series and incorporate these predictions into the adaptive acquisition and reconstruction methods, closing the feedback loop. In addition to developing adaptive versions of echo-planar imaging (EPI) and spiral pulse sequences widely used in fMRI, we propose extending the adaptive approach to accelerated parallel imaging acquisitions to enable higher resolution activation maps. These acquisitions accelerate imaging by undersampling the frequency domain (k-space) and using the redundancy from parallel receiver coils to undo aliasing in the resulting images. Conventional accelerated parallel imaging methods like GRAPPA are particularly susceptible to motion because the calibrated kernels used for interpolating missing k-space frequencies become inaccurate. Accelerated parallel imaging methods also greatly amplify the noise in the data, making statistical analysis unreliable at high accelerations. Thus, the third aim is to adjust the accelerated parallel imaging acquisition and reconstruction for motion and noise amplification to yield greatly improved quality images from substantially undersampled data. Altogether, we aim to provide novel acquisition and reconstruction techniques that are more robust to motion and observation noise and have greater resolution than conventional fMRI.